Statistical Inference for Complex Time Series Data

  • Rainer Dahlhaus

    Universität Heidelberg, Germany
  • Oliver Linton

    University of Cambridge, United Kingdom
  • Wei-Biao Wu

    University of Chicago, USA
  • Qiwei Yao

    London School of Economics, UK


During recent years the focus of scientific interest has turned from low dimensional stationary time series to nonstationary time series and high dimensional time series. In addition new methodological challenges are coming from high frequency finance where data are recorded and analyzed on a millisecond basis. The three topics “nonstationarity”, “high dimensionality” and “high frequency” are on the forefront of present research in time series analysis. The topics also have some overlap in that there already exists work on the intersection of these three topics, e.g. on locally stationary diffusion models, on high dimensional covariance matrices for high frequency data, or on multivariate dynamic factor models for nonstationary processes. The aim of the workshop was to bring together researchers from time series analysis, nonparametric statistics, econometrics and empirical finance to work on these topics. This aim was successfully achieved and the workshops was very well attended.

Cite this article

Rainer Dahlhaus, Oliver Linton, Wei-Biao Wu, Qiwei Yao, Statistical Inference for Complex Time Series Data. Oberwolfach Rep. 10 (2013), no. 3, pp. 2749–2823

DOI 10.4171/OWR/2013/48